Deep Learning Solutions for Classifying Patients on Opioid Use

Abstract

Opioid analgesics, as commonly prescribed medications used for relieving pain in patients, are especially prevalent in US these years. However, an increasing amount of opioid misuse and abuse have caused lots of consequences. Researchers and clinicians have attempted to discover the factors leading to opioid long-term use, dependence, and abuse, but only limited incidents are understood from previous works. Motivated by recent successes of deep learning and the abundant amount of electronic health records, we apply state-of-the-art deep and recurrent neural network models on a dataset of more than one hundred thousand opioid users. Our models are shown to achieve robust and superior results on classifying opioid users, and are able to extract key factors for different opioid user groups. This work is also a good demonstration on adopting novel deep learning methods for real-world health care problems.

Figure 1.

An illustration of the proposed pipeline from raw cohort data to final prediction. Left: DNN prediction model for data with temporal sum-pooling; Right: RNN prediction model for data with temporal segmentation..